Analyzing Ad Prevalence, Characteristics, and Compliance in Alexa Skills

With the rapid adoption of smart voice assistants like Amazon Alexa and the potential for more growth with large language model-powered assistants, as well as the introduction of “advertising ID” within Alexa, it is inevitable that advertisements (ads) will become prevalent on such platforms if not already. Although Alexa permits third-party developers to include ads within voice apps (known as “skills”) and enables targeted advertisement through ad identifiers, Alexa also lists an ad policy that restricts ads within skill responses, notifications, or reminders except in defined cases. However, it remains unclear whether all developers comply with these policies or attempt to bypass vetting processes to publish noncompliant ads. This paper presents the first large-scale analysis of advertising on the Alexa platform, examining ad prevalence, characteristics, and adherence to platform policies. We introduce an automated ad detection method using a fine-tuned large language model (LLM) with 88.92% accuracy and, using chain-of-thought (CoT) prompting, achieve 94.52% accuracy in identifying potential policy-violating ads. Analyzing 45,477 Alexa skills, we find that 13.58% include ads or promotional content, with themes such as travel and entertainment. Notably, some ads come from skills by Amazon-promoted agencies like “Vixen Labs” while others are generated by agencies solely focused on voice assistant platforms, such as “Skilled Creative.” Our model identifies approximately 29.18% of ads as possible policy violations. We reported our findings to Amazon, resulting in a bug bounty reward. The proposed system aims to enhance Alexa's vetting by automatically flagging potential ad violations and demonstrates how fine-tuned LLMs can support policy enforcement on voice platforms.